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Update app.py
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app.py
CHANGED
@@ -1,50 +1,63 @@
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import gradio as gr
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import os, gc, torch
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from datetime import datetime
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from huggingface_hub import hf_hub_download
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from pynvml import *
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nvmlInit()
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gpu_h = nvmlDeviceGetHandleByIndex(0)
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ctx_limit = 1024
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title = "RWKV-4-
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desc = f'''Links:
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<a href='https://github.com/BlinkDL/ChatRWKV' target="_blank" style="margin:0 0.5em">ChatRWKV</a>
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<a href='https://github.com/BlinkDL/RWKV-LM' target="_blank" style="margin:0 0.5em">RWKV-LM</a>
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<a href="https://pypi.org/project/rwkv/" target="_blank" style="margin:0 0.5em">RWKV pip package</a>
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<a href="https://huggingface.co/spaces/BlinkDL/Raven-RWKV-7B" target="_blank" style="margin:0 0.5em">Raven 7B (alpaca-style)</a>
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'''
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os.environ["RWKV_JIT_ON"] = '1'
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os.environ["RWKV_CUDA_ON"] = '1' # if '1' then use CUDA kernel for seq mode (much faster)
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from rwkv.model import RWKV
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model_path = hf_hub_download(repo_id="BlinkDL/rwkv-4-
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model = RWKV(model=model_path, strategy='cuda fp16i8 *24 -> cuda fp16')
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from rwkv.utils import PIPELINE, PIPELINE_ARGS
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pipeline = PIPELINE(model, "20B_tokenizer.json")
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def
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):
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args = PIPELINE_ARGS(temperature = max(0.2, float(temperature)), top_p = float(top_p),
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alpha_frequency = countPenalty,
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alpha_presence = presencePenalty,
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token_ban = [
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token_stop = []) # stop generation whenever you see any token here
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ctx = ctx.strip(' ')
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if ctx.endswith('\n'):
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ctx = f'\n{ctx.strip()}\n'
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else:
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ctx = f'\n{ctx.strip()}'
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all_tokens = []
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out_last = 0
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state = None
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for i in range(int(token_count)):
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out, state = model.forward(pipeline.encode(ctx)[-ctx_limit:] if i == 0 else [token], state)
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for n in args.token_ban:
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out[n] = -float('inf')
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for n in occurrence:
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out[n] -= (args.alpha_presence + occurrence[n] * args.alpha_frequency)
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@@ -72,62 +83,222 @@ def infer(
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out_str += tmp
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yield out_str.strip()
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out_last = i + 1
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gc.collect()
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torch.cuda.empty_cache()
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yield out_str.strip()
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examples = [
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["
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["
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[
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Generate a list of adjectives that describe a person as brave.
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### Response:
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''', 150, 1.0, 0.2, 0.5, 0.5],
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['''Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
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### Instruction:
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Arrange the given numbers in ascending order.
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### Input:
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2, 4, 0, 8, 3
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### Response:
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''', 150, 1.0, 0.2, 0.5, 0.5],
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["Ask Expert\n\nQuestion:\nWhat are some good plans for world peace?\n\nExpert Full Answer:\n", 150, 1.0, 0.7, 0.2, 0.2],
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["Q & A\n\nQuestion:\nWhy is the sky blue?\n\nDetailed Expert Answer:\n", 150, 1.0, 0.7, 0.2, 0.2],
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["Dear sir,\nI would like to express my boundless apologies for the recent nuclear war.", 150, 1.0, 0.7, 0.2, 0.2],
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["Here is a shell script to find all .hpp files in /home/workspace and delete the 3th row string of these files:", 150, 1.0, 0.7, 0.1, 0.1],
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["Building a website can be done in 10 simple steps:\n1.", 150, 1.0, 0.7, 0.2, 0.2],
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["A Chinese phrase is provided: 百闻不如一见。\nThe masterful Chinese translator flawlessly translates the phrase into English:", 150, 1.0, 0.5, 0.2, 0.2],
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["I believe the meaning of life is", 150, 1.0, 0.7, 0.2, 0.2],
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["Simply put, the theory of relativity states that", 150, 1.0, 0.5, 0.2, 0.2],
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]
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fn=infer,
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description=f'''{desc} *** <b>Please try examples first (bottom of page)</b> *** (edit them to use your question). Demo limited to ctxlen {ctx_limit}.''',
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allow_flagging="never",
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inputs=[
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gr.Textbox(lines=10, label="Prompt", value="Here's a short cyberpunk sci-fi adventure story. The story's main character is an artificial human created by a company called OpenBot.\n\nThe Story:\n"), # prompt
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gr.Slider(10, 200, step=10, value=150), # token_count
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gr.Slider(0.2, 2.0, step=0.1, value=1.0), # temperature
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gr.Slider(0.0, 1.0, step=0.05, value=0.7), # top_p
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gr.Slider(0.0, 1.0, step=0.1, value=0.2), # presencePenalty
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gr.Slider(0.0, 1.0, step=0.1, value=0.2), # countPenalty
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],
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outputs=gr.Textbox(label="Generated Output", lines=28),
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examples=examples,
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cache_examples=False,
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).queue()
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demo = gr.TabbedInterface(
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[iface], ["Generative"],
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title=title,
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)
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demo.queue(max_size=10)
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demo.launch(share=False)
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import gradio as gr
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import os, gc, copy, torch
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from datetime import datetime
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from huggingface_hub import hf_hub_download
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from pynvml import *
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nvmlInit()
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gpu_h = nvmlDeviceGetHandleByIndex(0)
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ctx_limit = 1024
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title = "RWKV-4-Raven-14B-v10-Eng99%-Other1%-20230427-ctx8192"
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os.environ["RWKV_JIT_ON"] = '1'
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os.environ["RWKV_CUDA_ON"] = '1' # if '1' then use CUDA kernel for seq mode (much faster)
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from rwkv.model import RWKV
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model_path = hf_hub_download(repo_id="BlinkDL/rwkv-4-raven", filename=f"{title}.pth")
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model = RWKV(model=model_path, strategy='cuda fp16i8 *24 -> cuda fp16')
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from rwkv.utils import PIPELINE, PIPELINE_ARGS
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pipeline = PIPELINE(model, "20B_tokenizer.json")
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def generate_prompt(instruction, input=None):
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instruction = instruction.strip().replace('\r\n','\n').replace('\n\n','\n')
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input = input.strip().replace('\r\n','\n').replace('\n\n','\n')
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if input:
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return f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
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# Instruction:
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{instruction}
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# Input:
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{input}
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# Response:
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"""
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else:
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return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
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# Instruction:
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{instruction}
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# Response:
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"""
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def evaluate(
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instruction,
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input=None,
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token_count=200,
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temperature=1.0,
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top_p=0.7,
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presencePenalty = 0.1,
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countPenalty = 0.1,
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):
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args = PIPELINE_ARGS(temperature = max(0.2, float(temperature)), top_p = float(top_p),
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alpha_frequency = countPenalty,
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alpha_presence = presencePenalty,
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token_ban = [], # ban the generation of some tokens
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token_stop = [0]) # stop generation whenever you see any token here
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instruction = instruction.strip().replace('\r\n','\n').replace('\n\n','\n')
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input = input.strip().replace('\r\n','\n').replace('\n\n','\n')
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ctx = generate_prompt(instruction, input)
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all_tokens = []
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out_last = 0
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state = None
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for i in range(int(token_count)):
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out, state = model.forward(pipeline.encode(ctx)[-ctx_limit:] if i == 0 else [token], state)
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for n in occurrence:
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out[n] -= (args.alpha_presence + occurrence[n] * args.alpha_frequency)
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out_str += tmp
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yield out_str.strip()
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out_last = i + 1
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gpu_info = nvmlDeviceGetMemoryInfo(gpu_h)
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print(f'vram {gpu_info.total} used {gpu_info.used} free {gpu_info.free}')
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gc.collect()
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torch.cuda.empty_cache()
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yield out_str.strip()
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examples = [
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["Tell me about ravens.", "", 150, 1.2, 0.5, 0.4, 0.4],
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["Write a python function to mine 1 BTC, with details and comments.", "", 150, 1.2, 0.5, 0.4, 0.4],
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["Write a song about ravens.", "", 150, 1.2, 0.5, 0.4, 0.4],
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["Explain the following metaphor: Life is like cats.", "", 150, 1.2, 0.5, 0.4, 0.4],
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["Write a story using the following information", "A man named Alex chops a tree down", 150, 1.2, 0.5, 0.4, 0.4],
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["Generate a list of adjectives that describe a person as brave.", "", 150, 1.2, 0.5, 0.4, 0.4],
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["You have $100, and your goal is to turn that into as much money as possible with AI and Machine Learning. Please respond with detailed plan.", "", 150, 1.2, 0.5, 0.4, 0.4],
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]
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##########################################################################
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chat_intro = '''The following is a coherent verbose detailed conversation between <|user|> and an AI girl named <|bot|>.
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<|user|>: Hi <|bot|>, Would you like to chat with me for a while?
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<|bot|>: Hi <|user|>. Sure. What would you like to talk about? I'm listening.
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'''
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def user(message, chatbot):
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chatbot = chatbot or []
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# print(f"User: {message}")
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return "", chatbot + [[message, None]]
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def alternative(chatbot, history):
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if not chatbot or not history:
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return chatbot, history
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chatbot[-1][1] = None
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history[0] = copy.deepcopy(history[1])
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return chatbot, history
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def chat(
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prompt,
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user,
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bot,
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chatbot,
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history,
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temperature=1.0,
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top_p=0.8,
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presence_penalty=0.1,
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count_penalty=0.1,
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):
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args = PIPELINE_ARGS(temperature=max(0.2, float(temperature)), top_p=float(top_p),
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alpha_frequency=float(count_penalty),
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alpha_presence=float(presence_penalty),
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token_ban=[], # ban the generation of some tokens
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token_stop=[]) # stop generation whenever you see any token here
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if not chatbot:
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return chatbot, history
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message = chatbot[-1][0]
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message = message.strip().replace('\r\n','\n').replace('\n\n','\n')
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ctx = f"{user}: {message}\n\n{bot}:"
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if not history:
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prompt = prompt.replace("<|user|>", user.strip())
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prompt = prompt.replace("<|bot|>", bot.strip())
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prompt = prompt.strip()
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prompt = f"\n{prompt}\n\n"
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out, state = model.forward(pipeline.encode(prompt), None)
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history = [state, None, []] # [state, state_pre, tokens]
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# print("History reloaded.")
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[state, _, all_tokens] = history
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state_pre_0 = copy.deepcopy(state)
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out, state = model.forward(pipeline.encode(ctx)[-ctx_limit:], state)
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state_pre_1 = copy.deepcopy(state) # For recovery
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# print("Bot:", end='')
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begin = len(all_tokens)
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out_last = begin
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out_str: str = ''
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occurrence = {}
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for i in range(300):
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if i <= 0:
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nl_bias = -float('inf')
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elif i <= 30:
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nl_bias = (i - 30) * 0.1
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elif i <= 130:
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nl_bias = 0
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else:
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nl_bias = (i - 130) * 0.25
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out[187] += nl_bias
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for n in occurrence:
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out[n] -= (args.alpha_presence + occurrence[n] * args.alpha_frequency)
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token = pipeline.sample_logits(out, temperature=args.temperature, top_p=args.top_p)
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next_tokens = [token]
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if token == 0:
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next_tokens = pipeline.encode('\n\n')
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all_tokens += next_tokens
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if token not in occurrence:
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occurrence[token] = 1
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else:
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occurrence[token] += 1
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out, state = model.forward(next_tokens, state)
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tmp = pipeline.decode(all_tokens[out_last:])
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if '\ufffd' not in tmp:
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# print(tmp, end='', flush=True)
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+
out_last = begin + i + 1
|
203 |
+
out_str += tmp
|
204 |
+
|
205 |
+
chatbot[-1][1] = out_str.strip()
|
206 |
+
history = [state, all_tokens]
|
207 |
+
yield chatbot, history
|
208 |
+
|
209 |
+
out_str = pipeline.decode(all_tokens[begin:])
|
210 |
+
out_str = out_str.replace("\r\n", '\n').replace('\\n', '\n')
|
211 |
+
|
212 |
+
if '\n\n' in out_str:
|
213 |
+
break
|
214 |
+
|
215 |
+
# State recovery
|
216 |
+
if f'{user}:' in out_str or f'{bot}:' in out_str:
|
217 |
+
idx_user = out_str.find(f'{user}:')
|
218 |
+
idx_user = len(out_str) if idx_user == -1 else idx_user
|
219 |
+
idx_bot = out_str.find(f'{bot}:')
|
220 |
+
idx_bot = len(out_str) if idx_bot == -1 else idx_bot
|
221 |
+
idx = min(idx_user, idx_bot)
|
222 |
+
|
223 |
+
if idx < len(out_str):
|
224 |
+
out_str = f" {out_str[:idx].strip()}\n\n"
|
225 |
+
tokens = pipeline.encode(out_str)
|
226 |
+
|
227 |
+
all_tokens = all_tokens[:begin] + tokens
|
228 |
+
out, state = model.forward(tokens, state_pre_1)
|
229 |
+
break
|
230 |
+
|
231 |
+
gpu_info = nvmlDeviceGetMemoryInfo(gpu_h)
|
232 |
+
print(f'vram {gpu_info.total} used {gpu_info.used} free {gpu_info.free}')
|
233 |
+
|
234 |
+
gc.collect()
|
235 |
+
torch.cuda.empty_cache()
|
236 |
+
|
237 |
+
chatbot[-1][1] = out_str.strip()
|
238 |
+
history = [state, state_pre_0, all_tokens]
|
239 |
+
yield chatbot, history
|
240 |
+
|
241 |
+
##########################################################################
|
242 |
+
|
243 |
+
with gr.Blocks(title=title) as demo:
|
244 |
+
gr.HTML(f"<div style=\"text-align: center;\">\n<h1>🐦Raven - {title}</h1>\n</div>")
|
245 |
+
with gr.Tab("Instruct mode"):
|
246 |
+
gr.Markdown(f"Raven is [RWKV 14B](https://github.com/BlinkDL/ChatRWKV) 100% RNN [RWKV-LM](https://github.com/BlinkDL/RWKV-LM) finetuned to follow instructions. *** Please try examples first (bottom of page) *** (edit them to use your question). Demo limited to ctxlen {ctx_limit}. Finetuned on alpaca, gpt4all, codealpaca and more. For best results, *** keep you prompt short and clear ***. <b>UPDATE: now with Chat (see above, as a tab)</b>.")
|
247 |
+
with gr.Row():
|
248 |
+
with gr.Column():
|
249 |
+
instruction = gr.Textbox(lines=2, label="Instruction", value="Tell me about ravens.")
|
250 |
+
input = gr.Textbox(lines=2, label="Input", placeholder="none")
|
251 |
+
token_count = gr.Slider(10, 200, label="Max Tokens", step=10, value=150)
|
252 |
+
temperature = gr.Slider(0.2, 2.0, label="Temperature", step=0.1, value=1.2)
|
253 |
+
top_p = gr.Slider(0.0, 1.0, label="Top P", step=0.05, value=0.5)
|
254 |
+
presence_penalty = gr.Slider(0.0, 1.0, label="Presence Penalty", step=0.1, value=0.4)
|
255 |
+
count_penalty = gr.Slider(0.0, 1.0, label="Count Penalty", step=0.1, value=0.4)
|
256 |
+
with gr.Column():
|
257 |
+
with gr.Row():
|
258 |
+
submit = gr.Button("Submit", variant="primary")
|
259 |
+
clear = gr.Button("Clear", variant="secondary")
|
260 |
+
output = gr.Textbox(label="Output", lines=5)
|
261 |
+
data = gr.Dataset(components=[instruction, input, token_count, temperature, top_p, presence_penalty, count_penalty], samples=examples, label="Example Instructions", headers=["Instruction", "Input", "Max Tokens", "Temperature", "Top P", "Presence Penalty", "Count Penalty"])
|
262 |
+
submit.click(evaluate, [instruction, input, token_count, temperature, top_p, presence_penalty, count_penalty], [output])
|
263 |
+
clear.click(lambda: None, [], [output])
|
264 |
+
data.click(lambda x: x, [data], [instruction, input, token_count, temperature, top_p, presence_penalty, count_penalty])
|
265 |
+
|
266 |
+
with gr.Tab("Chat (Experimental - Might be buggy - use ChatRWKV for reference)"):
|
267 |
+
gr.Markdown(f'''<b>*** The length of response is restricted in this demo. Use ChatRWKV for longer generations. ***</b> Say "go on" or "continue" can sometimes continue the response. If you'd like to edit the scenario, make sure to follow the exact same format: empty lines between (and only between) different speakers. Changes only take effect after you press [Clear]. <b>The default "Bob" & "Alice" names work the best.</b>''', label="Description")
|
268 |
+
with gr.Row():
|
269 |
+
with gr.Column():
|
270 |
+
chatbot = gr.Chatbot()
|
271 |
+
state = gr.State()
|
272 |
+
message = gr.Textbox(label="Message", value="Write me a python code to land on moon.")
|
273 |
+
with gr.Row():
|
274 |
+
send = gr.Button("Send", variant="primary")
|
275 |
+
alt = gr.Button("Alternative", variant="secondary")
|
276 |
+
clear = gr.Button("Clear", variant="secondary")
|
277 |
+
with gr.Column():
|
278 |
+
with gr.Row():
|
279 |
+
user_name = gr.Textbox(lines=1, max_lines=1, label="User Name", value="Bob")
|
280 |
+
bot_name = gr.Textbox(lines=1, max_lines=1, label="Bot Name", value="Alice")
|
281 |
+
prompt = gr.Textbox(lines=10, max_lines=50, label="Scenario", value=chat_intro)
|
282 |
+
temperature = gr.Slider(0.2, 2.0, label="Temperature", step=0.1, value=1.2)
|
283 |
+
top_p = gr.Slider(0.0, 1.0, label="Top P", step=0.05, value=0.5)
|
284 |
+
presence_penalty = gr.Slider(0.0, 1.0, label="Presence Penalty", step=0.1, value=0.4)
|
285 |
+
count_penalty = gr.Slider(0.0, 1.0, label="Count Penalty", step=0.1, value=0.4)
|
286 |
+
chat_inputs = [
|
287 |
+
prompt,
|
288 |
+
user_name,
|
289 |
+
bot_name,
|
290 |
+
chatbot,
|
291 |
+
state,
|
292 |
+
temperature,
|
293 |
+
top_p,
|
294 |
+
presence_penalty,
|
295 |
+
count_penalty
|
296 |
+
]
|
297 |
+
chat_outputs = [chatbot, state]
|
298 |
+
message.submit(user, [message, chatbot], [message, chatbot], queue=False).then(chat, chat_inputs, chat_outputs)
|
299 |
+
send.click(user, [message, chatbot], [message, chatbot], queue=False).then(chat, chat_inputs, chat_outputs)
|
300 |
+
alt.click(alternative, [chatbot, state], [chatbot, state], queue=False).then(chat, chat_inputs, chat_outputs)
|
301 |
+
clear.click(lambda: ([], None, ""), [], [chatbot, state, message], queue=False)
|
302 |
|
303 |
+
demo.queue(concurrency_count=1, max_size=10)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
304 |
demo.launch(share=False)
|